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The search of “canonical” explanations for the cerebral cortex

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Abstract

This paper addresses a fundamental line of research in neuroscience: the identification of a putative neural processing core of the cerebral cortex, often claimed to be “canonical”. This “canonical” core would be shared by the entire cortex, and would explain why it is so powerful and diversified in tasks and functions, yet so uniform in architecture. The purpose of this paper is to analyze the search for canonical explanations over the past 40 years, discussing the theoretical frameworks informing this research. It will highlight a bias that, in my opinion, has limited the success of this research project, that of overlooking the dimension of cortical development. The earliest explanation of the cerebral cortex as canonical was attempted by David Marr, deriving putative cortical circuits from general mathematical laws, loosely following a deductive-nomological account. Although Marr’s theory turned out to be incorrect, one of its merits was to have put the issue of cortical circuit development at the top of his agenda. This aspect has been largely neglected in much of the research on canonical models that has followed. Models proposed in the 1980s were conceived as mechanistic. They identified a small number of components that interacted as a basic circuit, with each component defined as a function. More recent models have been presented as idealized canonical computations, distinct from mechanistic explanations, due to the lack of identifiable cortical components. Currently, the entire enterprise of coming up with a single canonical explanation has been criticized as being misguided, and the premise of the uniformity of the cortex has been strongly challenged. This debate is analyzed here. The legacy of the canonical circuit concept is reflected in both positive and negative ways in recent large-scale brain projects, such as the Human Brain Project. One positive aspect is that these projects might achieve the aim of producing detailed simulations of cortical electrical activity, a negative one regards whether they will be able to find ways of simulating how circuits actually develop.

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References

  • Abbott, A. (2014). Row hits flagship brain plan. Nature, 511, 133–134.

    Google Scholar 

  • Adelson, E. H., & Bergen, J. R. (1985). Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America, 2, 284–299.

    Google Scholar 

  • Anzai, A., Peng, X., & Van Essen, D. C. (2007). Neurons in monkey visual area V2 encode combinations of orientations. Nature Neuroscience, 10, 1313–1321.

    Google Scholar 

  • Baker, A. (2005). Are there genuine mathematical explanations of physical phenomena? Mind, 114, 223–238.

    Google Scholar 

  • Bauer, R., Zubler, F., Pfister, S., Hauri, A., Pfeiffer, M., Muir, D. R., et al. (2014). Developmental self-construction and -configuration of functional neocortical neuronal networks. PLoS Computational Biology, 10, e1003994.

    Google Scholar 

  • Bechtel, W. (1982). Two common errors in explaining biological and psychological phenomena. Philosophy of Science, 49, 549–574.

    Google Scholar 

  • Bechtel, W. (2015). Generalizing mechanistic explanations using graph-theoretic representations. In P.-A. Braillard & C. Malaterre (Eds.), Explanation in biology—An enquiry into the diversity of explanatory patterns in the life sciences (pp. 199–225). Berlin: Springer.

    Google Scholar 

  • Bergeron, V. (2007). Anatomical and functional modularity in cognitive science: Shifting the focus. Philosophical Psychology, 20, 175–195.

    Google Scholar 

  • Berlin, R. (1858). Beitrag zur Structurlehre der Grosshirnwindungen. Ph.D. thesis, Medicinischen Fakultät zu Erlangen.

  • Betz, V. A. (1874). Anatomischer Nachweis zweier Gehirncentra. Zentralblatt medicinischen Wissenschaften, 12, 578–599.

    Google Scholar 

  • Binzegger, T., Douglas, R. J., & Martin, K. A. (2004). A quantitative map of the circuit of cat primary visual cortex. Journal of Neuroscience, 24, 8441–8453.

    Google Scholar 

  • Binzegger, T., Douglas, R. J., & Martin, K. A. (2009). Topology and dynamics of the canonical circuit of cat V1. Neural Networks, 22, 1071–1078.

    Google Scholar 

  • Boone, W., & Piccinini, G. (2016). The cognitive neuroscience revolution. Synthese, 193, 1509–1534.

    Google Scholar 

  • Borck, C. (2001). Electricity as a medium of psychic life: Electrotechnological adventures into psychodiagnosis in weimar germany. Science in Context, 14, 565–590.

    Google Scholar 

  • Bower, J. (2005). Looking for Newton: Realistic modeling in modern biology. Brains, Minds, Media, 1, bmm217 (urn:nbn:de:0009-3-2177).

  • Braak, H. (1974). On the structure of the human archicortex. i. the cornu ammonis. a Golgi and pigment architectonic study. Cell Tissue Research, 152, 349–383.

    Google Scholar 

  • Braillard, P.-A., & Malaterre, C. (Eds.). (2015a). Explanation in biology—An enquiry into the diversity of explanatory patterns in the life sciences. Berlin: Springer.

    Google Scholar 

  • Braillard, P.-A., & Malaterre, C. (2015b). Explanation in biology: An introduction. In Braillard and Malaterre (2015a) (pp. 1–28).

    Google Scholar 

  • Brazier, M. (1961). A history of the electrical activity of the brain: The first half-century. New York: Macmillan.

    Google Scholar 

  • Brindley, G. S., & Lewin, W. (1968). The sensations produced by electrical stimulation of the visual cortex. Journal of Neurophysiology, 196, 479–493.

    Google Scholar 

  • Brodmann, K. (1903). Beiträge zur histologischen lokalisation der Grosshirnrinde, 1: die Regio Rolandica; 2: der Calcarintypus. Journal für Psychologie und Neurologie, 2(79–107), 133–159.

    Google Scholar 

  • Brodmann, K. (1909). Vergleichende Lokalisationslehre der Grosshirmrinde. Leipzig: Barth.

    Google Scholar 

  • Brown, A. G., Fyffe, R., Noble, R., Rose, P., & Snow, P. (1980). The density, distribution and topographical organization of spinocervical tract neurones in the cat. Journal of Physiology, 300, 409–428.

    Google Scholar 

  • Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186–198.

    Google Scholar 

  • Buonomano, D. V., & Merzenich, M. M. (1998). Cortical plasticity: From synapses to maps. Annual Review of Neuroscience, 21, 149–186.

    Google Scholar 

  • Burnston, D. C. (2016a). Computational neuroscience and localized neural function. Synthese, 193, 3741–3762.

    Google Scholar 

  • Burnston, D. C. (2016b). A contextualist approach to functional localization in the brain. Biology and Philosophy, 31, 527–550.

    Google Scholar 

  • Buxhoeveden, D. P., & Casanova, M. F. (2002). The minicolumn hypothesis in neuroscience. Brain, 125, 935–951.

    Google Scholar 

  • Carandini, M., & Heeger, D. (2012). Normalization as a canonical neural computation. Nature Reviews Neuroscience, 13, 51–62.

    Google Scholar 

  • Carlo, C. N., & Stevens, C. F. (2013). Structural uniformity of neocortex, revisited. Proceedings of the Natural Academy of Science USA, 110, 719–725.

    Google Scholar 

  • Casanova, M. F. (2013). Canonical circuits of the cerebral cortex as enablers of neuroprosthetics. Journal of Experimental Social Psychology, 49, 719–725.

    Google Scholar 

  • Casanova, M. F., & Opris, I. (Eds.). (2015). Recent advances on the modular organization of the cortex. Berlin: Springer.

    Google Scholar 

  • Chalupa, L., & Werner, J. (Eds.). (2003). The visual neurosciences. Cambridge, MA: MIT Press.

    Google Scholar 

  • Chirimuuta, M. (2014). Minimal models and canonical neural computations: The distinctness of computational explanation in neuroscience. Synthese, 191, 127–153.

    Google Scholar 

  • Chirimuuta, M. (2017). Explanation in computational neuroscience: Causal and non-causal. British Journal for the Philosophy of Science, axw034.

  • Church, A. (1941). The calculi of lambda conversion. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Churchland, P. S., & Sejnowski, T. (1994). The computational brain. Cambridge, MA: MIT Press.

    Google Scholar 

  • Copeland, J. B., Posy, C. J., & Shagrir, O. (Eds.). (2013). Computability: Turing, Gödel, Church, and Beyond. Cambridge, MA: MIT Press.

    Google Scholar 

  • Cowan, J. D. (1991). Commentary on a theory for cerebral neocortex. In L. Vaina (Ed.), From the retina to the neocortex (pp. 203–209). Boston: Birkhäuser.

    Google Scholar 

  • Craver, C. F. (2001). Role functions, mechanisms, and hierarchy. Philosophy of Science, 68, 53–74.

    Google Scholar 

  • Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford: Oxford University Press.

    Google Scholar 

  • Craver, C. F., & Darden, L. (2013). In search of mechanisms: Discoveries across the life sciences. Chicago, IL: University of Chicago Press.

    Google Scholar 

  • Cummins, R. (1975). Functional analysis. Journal of Philosophy, 72, 741–765.

    Google Scholar 

  • Daly, C., & Langford, S. (2009). Mathematical explanation and indispensability arguments. Philosophical Quarterly, 59, 641–658.

    Google Scholar 

  • Daugman, J. G. (1980). Two-dimensional spectral analysis of cortical receptive field profiles. Vision Research, 20, 847–856.

    Google Scholar 

  • Daugman, J. G. (1985). Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America, 2, 1160–1169.

    Google Scholar 

  • Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience. Cambridge, MA: MIT Press.

    Google Scholar 

  • DeFelipe, J. (2015). The anatomical problem posed by brain complexity and size: A potential solution. Frontiers in Neuroanatomy, 9, Article 104.

  • DeFelipe, J., Douglas, R. J., Hill, S. L., Lein, E. S., Martin, K. A. C., Rockland, K. S., et al. (2016). Comments and general discussion on “the anatomical problem posed by brain complexity and size: A potential solution”. Frontiers in Neuroanatomy, 10, Article 60.

  • Dehaene, S. (2014). Consciousness and the brain: Deciphering how the brain codes our thoughts. New York: Viking Adult.

    Google Scholar 

  • Douglas, R. J., Mahowald, M. A., & Martin, K. A. C. (1994). Hybrid analog-digital architectures for neuromorphic systems. In Proceedings of the IEEE international conference on neural networks (pp. 1848–1853).

  • Douglas, R. J., Markram, H., & Martin, K. (2004). Neocortex. In G. M. Shepherd (Ed.), The synaptic organization of the brain (5th ed., pp. 499–558). Oxford: Oxford University Press.

    Google Scholar 

  • Douglas, R. J., & Martin, K. A. (1991). Opening the grey box. Trends in Neuroscience, 14, 286–293.

    Google Scholar 

  • Douglas, R. J., & Martin, K. A. (1992). In search of the canonical microcircuits of neocortex. In R. Lent (Ed.), The visual system from genesis to maturity (pp. 213–232). Berlin: Springer.

    Google Scholar 

  • Douglas, R. J., & Martin, K. A. (2004). Neuronal circuits of the neocortex. Annual Review of Neuroscience, 27, 419–451.

    Google Scholar 

  • Douglas, R. J., Martin, K. A., & Whitteridge, D. (1989). A canonical microcircuit for neocortex. Neural Computation, 1, 480–488.

    Google Scholar 

  • du Bois-Reymond, E. (1849). Untersuchungen über Thierische Elektricität. Berlin: G. Reimer.

    Google Scholar 

  • Eliasmith, C., & Trujillo, O. (2014). The use and abuse of large-scale brain models. Current Opinion in Neurobiology, 25, 1–6.

    Google Scholar 

  • Farhat, N. H. (2007). Corticonic models of brain mechanisms underlying cognition and intelligence. Physics of Life Reviews, 4, 223–252.

    Google Scholar 

  • Fodor, J. (1975). The language of thought. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Fresco, N. (2014). Physical computation and cognitive science. Berlin: Springer.

    Google Scholar 

  • Fuster, J. M. (2008). The Prefrontal Cortex (4th ed.). New York: Academic Press.

    Google Scholar 

  • Gabor, D. (1946). Theory of communication. Journal IEEE, 93, 429–459.

    Google Scholar 

  • Gao, W.-J., & Pallas, S. (1999). Cross-modal reorganization of horizontal connectivity in auditory cortex without altering thalamocortical projections. Journal of Neuroscience, 19, 7940–7950.

    Google Scholar 

  • Garson, J. (2016). A critical overview of biological functions. Berlin: Springer.

    Google Scholar 

  • Glennan, S. (1996). Mechanisms and the nature of causation. Erkenntnis, 44, 49–71.

    Google Scholar 

  • Grzywacz, N. M., & Yuille, A. L. (1990). A model for the estimate of local image velocity by cells in the visual cortex. Proceedings of the Royal Society of London B, 239, 129–161.

    Google Scholar 

  • Haberly, L. B., & Shepherd, G. M. (1973). Current-density analysis of summed evoked potentials in opossum prepyriform cortex. Journal of Neurophysiology, 36, 789–802.

    Google Scholar 

  • Haeusler, S., & Maass, W. (2007). A statistical analysis of information-processing properties of lamina-specific cortical microcircuit models. Cerebral Cortex, 17, 149–162.

    Google Scholar 

  • Haeusler, S., Schuch, K., & Maass, W. (2009). Motif distribution, dynamical properties, and computational performance of two data-based cortical microcircuit templates. Journal of Physiology – Paris, 21, 1229–1243.

    Google Scholar 

  • Hagmann, P., Cammoun, L., Gigandet, X., Gerhard, S., Grant, P. E., Wedeen, V., et al. (2010). MR connectomics: Principles and challenges. Journal of Neuroscience Methods, 194, 34–45.

    Google Scholar 

  • Harris, K. D., & Shepherd, G. M. (2015). The neocortical circuit: Themes and variations. Nature Neuroscience, 18, 170–181.

    Google Scholar 

  • Haueis, P. (2016). The life of the cortical column: Opening the domain of functional architecture of the cortex (1955–1981). History and Philosophy of the Life Science, 38, 2.

    Google Scholar 

  • Heeger, D. J. (1993). Modeling simple-cell direction selectivity with normalized, half-squared, linear operators. Journal of Neurophysiology, 70, 1885–1898.

    Google Scholar 

  • Hempel, C. G. (1965). Aspects of scientific explanation and other essays in the philosophy of science. New York: Free Press.

    Google Scholar 

  • Herculano-Houzel, S., Collins, C. E., Wong, P., Kaas, J. H., & Lent, R. (2008). The basic nonuniformity of the cerebral cortex. Proceedings of the Natural Academy of Science USA, 34, 12593–12598.

    Google Scholar 

  • Hines, M., & Carnevale, N. (1997). The NEURON simulation environment. Neural Computation, 9, 1179–1209.

    Google Scholar 

  • Holland, O., & Husbands, P. (2011). The origins of British cybernetics: The Ratio Club. Kybernetes, 40, 110–123.

    Google Scholar 

  • Hoorweg, J. L. (1898). Ueber die elektrischen Eigenschaften der Nerven. Pflügers Arch ges Physiol, 71, 128–157.

    Google Scholar 

  • Horton, J. C., & Adams, D. L. (2005). The cortical column: A structure without a function. Philosophical Transactions of the Royal Society B, 360, 837–862.

    Google Scholar 

  • Hubel, D., & Wiesel, T. (1959). Receptive fields of single neurones in the cat’s striate cortex. Journal of Physiology, 148, 574–591.

    Google Scholar 

  • Hubel, D., & Wiesel, T. (1963). Shape and arrangement of columns in cat’s striate cortex. Journal of Physiology, 165, 559–568.

    Google Scholar 

  • Hubel, D., & Wiesel, T. (1974). Uniformity of monkey striate cortex: A parallel relationship between field size, scatter, and magnification factor. Journal of Comparative Neurology, 158, 295–305.

    Google Scholar 

  • Jones, J. P., & Palmer, L. A. (1987). The two-dimensional spatial structure of simple receptive fields in cat striate cortex. Journal of Neurophysiology, 58, 1187–1211.

    Google Scholar 

  • Kaas, J. H. (2012). Evolution of columns, modules, and domains in the neocortex of primates. Proceedings of the Natural Academy of Science USA, 109, 10655–10660.

    Google Scholar 

  • Kaplan, D. M. (2011). Explanation and description in computational neuroscience. Synthese, 183, 339–373.

    Google Scholar 

  • Kaplan, D. M. (Ed.). (2018). Neural computation, multiple realizability, and the prospects for mechanistic explanation. In Explanation and integration in mind and brain science (pp. 164–189). Oxford: Oxford University Press.

    Google Scholar 

  • Kaplan, D. M., & Craver, C. F. (2011). Towards a mechanistic philosophy of neuroscience. In S. French & J. Saatsi (Eds.), Continuum companion to the philosophy of science (pp. 268–292). London: Continuum Press.

    Google Scholar 

  • Karbowski, J. (2014). Constancy and trade-offs in the neuroanatomical and metabolic design of the cerebral cortex. Frontiers in Neural Circuits, 8, 9.

    Google Scholar 

  • Kirchhoff, G. (1845). Ueber den Durchgang eines elektrischen Stromes durch eine Ebene, insbesonere durch eine kreisförmige. Poggendorff’s Annalen der Physik und Chemie, 64, 487–514.

    Google Scholar 

  • Kisvárday, Z., Martin, K. A., & Whitteridge, D. (1985). Synaptic connections of intracellularly filled clutch cells: A type of small basket cell in the visual cortex of the cat. Journal of Comparative Neurology, 241, 111–137.

    Google Scholar 

  • Kitcher, P. (1981). Explanatory unification. Philosophy of Science, 48, 507–531.

    Google Scholar 

  • Klein, C. (2017). Brain regions as difference-makers. Philosophical Psychology, 30, 1–20.

    Google Scholar 

  • Kouh, M., & Poggio, T. (2008). A canonical neural circuit for cortical nonlinear operations. Neural Computation, 20, 1427–1451.

    Google Scholar 

  • Lange, M. (2013). What makes a scientific explanation distinctively mathematical? British Journal for the Philosophy of Science, 64, 485–511.

    Google Scholar 

  • Lorente de Nó, R. (1938). Architectonics and structure of the cerebral cortex. In J. Fulton (Ed.), Physiology of the nervous system (pp. 291–330). Oxford: Oxford University Press.

    Google Scholar 

  • Lücke, J. (2009). Receptive field self-organization in a model of the fine structure in V1 cortical columns. Neural Computation, 21, 2805–2845.

    Google Scholar 

  • Maçarico da Costa, N., & Martin, K. A. C. (2010). Whose cortical column would that be? Frontiers in Neuroanatomy, 4, 16.

    Google Scholar 

  • Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–84.

    Google Scholar 

  • Mahowald, M. (1994). An analog VLSI system for stereoscopic vision. Dordrecht, NL: Kluwer.

    Google Scholar 

  • Marcus, G. F., Marblestone, A., & Dean, T. (2014). The atoms of neural computation. Science, 346, 551–552.

    Google Scholar 

  • Markram, H. (2006). The blue brain project. Nature Reviews Neuroscience, 7, 153–160.

    Google Scholar 

  • Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., et al. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell, 163, 456–492.

    Google Scholar 

  • Marr, D. (1970). A theory for cerebral neocortex. Proceedings of the Royal Society of London B, 176, 161–234.

    Google Scholar 

  • Marr, D. (1975). Review: Approaches to biological information processing. Science, 190, 875–876.

    Google Scholar 

  • Martin, K. A. C. (1988). The Wellcome Prize lecture—From single cells to simple circuits in the cerebral cortex. Quarterly Journal of Experimental Physiology, 73, 637–702.

    Google Scholar 

  • Martin, K. A., & Whitteridge, D. (1982). The morphology, function and intracortical projections of neurones in area 17 of the cat which receive monosynaptic input from the lateral geniculate nucleus (LGN). Journal of Physiology, 328, 37–38p.

    Google Scholar 

  • Martin, K. A., & Whitteridge, D. (1984a). Form, function, and intracortical projections of spiny neurones in the striate visual cortex of the cat. Journal of Physiology, 353, 463–504.

    Google Scholar 

  • Martin, K. A., & Whitteridge, D. (1984b). The relationship of receptive field properties to the dendritic shape of neurones in the cat striate cortex. Journal of Physiology, 356, 291–302.

    Google Scholar 

  • Martinotti, C. (1890). Beitrag zum Studium der Hirnrinde und dem Centralursprung der Nerven. Internationale Monatsschrift für Anatomie und Physiologie, 7, 69–90.

    Google Scholar 

  • Mead, C. (1989). Analog VLSI and neural systems. Reading, MA: Addison Wesley.

    Google Scholar 

  • Mehta, M. R., Quirk, M. C., & Wilson, M. A. (2000). Experience-dependent asymmetric shape of hippocampal receptive fields. Neuron, 25, 707–715.

    Google Scholar 

  • Meynert, T. H. (1869). Studien über die Bedeutung des zweifachen Rückenmarksursprunges aus dem Grosshirn. Sitzungsber Akad Wissensch, II Abt, 60, 447–462.

    Google Scholar 

  • Miikkulainen, R., Bednar, J., Choe, Y., & Sirosh, J. (2005). Computational maps in the visual cortex. New York: Springer.

    Google Scholar 

  • Miłkowski, M. (2013). Explaining the computational mind. Cambridge, MA: MIT Press.

    Google Scholar 

  • Miller, L. M., Escab, M. A., Read, H. L., & Schreiner, C. E. (2002a). Spectrotemporal receptive fields in the lemniscal auditory thalamus and cortex. Journal of Neurophysiology, 87, 516–527.

    Google Scholar 

  • Miller, E. K., Freedman, D. J., & Wallis, J. D. (2002b). The prefrontal cortex: Categories, concepts and cognition. Philosophical Transactions: Biological Sciences, 357, 1123–1136.

    Google Scholar 

  • Millikan, R. G. (1989). Biosemantic. Journal of Philosophy, 86, 242–265.

    Google Scholar 

  • Molnár, Z. (2013). Cortical columns. In J. L. R. Rubenstein & P. Rakic (Eds.), Comprehensive developmental neuroscience: Neural circuit development and function in the healthy and diseased brain (pp. 109–129). New York: Academic Press.

    Google Scholar 

  • Mountcastle, V. (1957). Modality and topographic properties of single neurons in cats somatic sensory cortex. Journal of Neurophysiology, 20, 408–434.

    Google Scholar 

  • Mountcastle, V. (1997). The columnar organization of the neocortex. Brain, 120, 701–722.

    Google Scholar 

  • Nauta, W. J., & Karten, H. J. (1970). A general profile of the vertebrate brain, with sidelights on the ancestry of cerebral cortex. In F. O. Schmitt (Ed.), The neurosciences: Second study program (pp. 1–7). New York: Rockfeller University Press.

    Google Scholar 

  • Nelson, R. J. (2002a). The somatosensory system: Deciphering the brain’s own body image. Boca Raton, FL: CRC Press.

    Google Scholar 

  • Nelson, S. B. (2002b). Cortical microcircuits: Diverse or canonical? Neuron, 36, 19–27.

    Google Scholar 

  • Newcomb, R. W. (1994). Some historical perspectives on early pulse coded neural network circuits. In M. E. Zaghloul, J. L. Meador, & R. W. Newcomb (Eds.), Silicon implementation of pulse coded neural networks (pp. 1–8). Berlin: Springer.

    Google Scholar 

  • Nieder, A. (2009). Prefrontal cortex and the evolution of symbolic reference. Current Opinion in Neurobiology, 19, 99–108.

    Google Scholar 

  • Noack, R. A. (2012). Solving the “human problem”: The frontal feedback model. Consciousness and Cognition, 21, 1043–1067.

    Google Scholar 

  • Papineau, D. (1993). Philsophical naturalism. Oxford: Basil Blackwell.

    Google Scholar 

  • Paynter, H., & Beaman, J. J. (1991). On the fall and rise of the circuit concept. Journal of the Franklin Institute, 328, 525–534.

    Google Scholar 

  • Paz, A. W. (2017). A mechanistic perspective on canonical neural computation. Philosophical Psychology, 30, 209–230.

    Google Scholar 

  • Peters, A., & Payne, B. R. (1993). Numerical relationships between geniculocortical afferents and pyramidal cell modules in cat primary visual cortex. Cerebral Cortex, 64, 467–478.

    Google Scholar 

  • Pettersen, K. H., & Einevoll, G. T. (2009). Neurophysics: What the telegrapher’s equation has taught us about the brain. In O. G. Martinsen & O. Jensen (Eds.), An anthology of developments in clinical engineering and bioimpedance—Festschrift for Sverre Grimnes (pp. 94–108). Oslo: Medisin-teknisk avdelings forlag.

    Google Scholar 

  • Phillips, C., Powell, T., & Shepherd, G. M. (1963). Responses of mitral cells to stimulation of the lateral olfactory tract in the rabbit. Journal of Physiology, 168, 65–88.

    Google Scholar 

  • Piccinini, G. (2006). Computational explanation in neuroscience. Synthese, 153, 343–353.

    Google Scholar 

  • Piccinini, G. (2007). Computational modeling vs. computational explanation: Is everything a Turing Machine, and does it matter to the philosophy of mind? Australasian Journal of Philosoph, 85, 93–115.

    Google Scholar 

  • Piccinini, G. (2015). Physical computation: A mechanistic account. Oxford: Oxford University Press.

    Google Scholar 

  • Plebe, A. (2012). A model of the response of visual area V2 to combinations of orientations. Network: Computation in Neural Systems, 23, 105–122.

    Google Scholar 

  • Potjans, T. C., & Diesmann, M. (2014). The cell-type specific cortical microcircuit: Relating structure and activity in a full-scale spiking network model. Cerebral Cortex, 24, 785–806.

    Google Scholar 

  • Priebe, N. J., & Ferster, D. (2006). Mechanisms underlying cross-orientation suppression in cat visual cortex. Nature Neuroscience, 9, 552–562.

    Google Scholar 

  • Prinz, J. (2006). Is the mind really modular? In R. J. Stainton (Ed.), Contemporary debates in cognitive science (pp. 22–36). Malden, MA: Blackwell Publishing.

    Google Scholar 

  • Pulvermüller, F. (2002). The neuroscience of language: On brain circuits of words and serial order. Cambridge: Cambridge University Press.

    Google Scholar 

  • Pylyshyn, Z. (1981). Computation and cognition: Issues in the foundations of cognitive science. Behavioral and Brain Science, 3, 111–150.

    Google Scholar 

  • Rakic, P. (1971). Guidance of neurons migrating to the fetal monkey neocortex. Brain Research, 33, 471–476.

    Google Scholar 

  • Rakic, P. (1995). Radial versus tangential migration of neuronal clones in the developing cerebral cortex. Proceedings of the Natural Academy of Science USA, 92, 323–327.

    Google Scholar 

  • Rakic, P. (2008). Confusing cortical columns. Proceedings of the Natural Academy of Science USA, 34, 12099–12100.

    Google Scholar 

  • Rall, W. (1957). Membrane time constant of motoneurons. Science, 126, 454.

    Google Scholar 

  • Rall, W. (1969). Time constants and electrotonic length of membrane cylinders and neurons. Biophysic Journal, 9, 1483–1508.

    Google Scholar 

  • Rall, W. (2006). Wilfrid rall. In L. R. Squire (Ed.), The history of neuroscience in autobiography (Vol. 5, pp. 552–661). Amsterdam: Elsevier.

    Google Scholar 

  • Rall, W., & Shepherd, G. M. (1968). Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. Journal of Neurophysiology, 31, 884–915.

    Google Scholar 

  • Ramón y Cajal, S. (1891). On the structure of the cerebral cortex in certain mammals. La Cellule, 7, 125–176.

    Google Scholar 

  • Rathkopf, C. A. (2013). Localization and intrinsic function. Philosophy of Science, 80, 1–21.

    Google Scholar 

  • Rauschecker, J. P., Tian, B., & Hauser, M. (1995). Processing of complex sounds in the macaque nonprimary auditory cortex. Science, 268, 111–114.

    Google Scholar 

  • Reynolds, J. H., Chelazzi, L., & Desimone, R. (1999). Competitive mechanisms subserve attention in macaque areas V2 and V4. Nature Neuroscience, 2, 1019–1025.

    Google Scholar 

  • Rockel, A., Hiorns, R., & Powell, T. (1974). Numbers of neurons through full depth of neocortex. Journal of Anatomy, 118, 371.

    Google Scholar 

  • Rockel, A., Hiorns, R., & Powell, T. (1980). The basic uniformity in structure of the neocortex. Brain, 103, 221–244.

    Google Scholar 

  • Rockland, K. S. (2011). Five points on columns. Frontiers in Neuroanatomy, 4, Article 22.

  • Roe, A. W., Garraghty, P., Esguerra, M., & Sur, M. (1990). A map of visual space induced in primary auditory cortex. Science, 250, 818–820.

    Google Scholar 

  • Roe, A. W., Garraghty, P., & Sur, M. (1987). Retinotectal W cell plasticity: Experimentally induced retinal projections to auditory thalamus in ferrets. Society for Neuroscience Abstracts, 13, 1023.

    Google Scholar 

  • Rose, D. (1979). Mechanisms underlying the receptive field properties of neurons in cat visual cortex. Vision Research, 19, 533–544.

    Google Scholar 

  • Rose, N., & Abi-Rached, J. M. (2013). Neuro: The new brain sciences and the management of the mind. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Rothschild, G., & Mizrahi, A. (2015). Global order and local disorder in brain maps. Annual Review of Neuroscience, 38, 247–268.

    Google Scholar 

  • Roux, E. (2014). The concept of function in modern physiology. Journal of Physiology, 592, 2245–2249.

    Google Scholar 

  • Shepherd, G. M. (1974). The synaptic organization of the brain. Oxford: Oxford University Press.

    Google Scholar 

  • Shepherd, G. M. (1979). The synaptic organization of the brain (2nd ed.). Oxford: Oxford University Press.

    Google Scholar 

  • Shepherd, G. M. (1988). A basic circuit for cortical organization. In M. S. Gazzaniga (Ed.), Perspectives on memory research (pp. 93–134). Cambridge, MA: MIT Press.

    Google Scholar 

  • Shepherd, G. M. (1991). Foundations of the neuron doctrine. Oxford: Oxford University Press.

    Google Scholar 

  • Shepherd, G. M. (2004a). Introduction to synaptic circuits. In G. M. Shepherd (Ed.), The synaptic organization of the brain (5th ed., pp. 1–38). Oxford: Oxford University Press.

    Google Scholar 

  • Shepherd, G. M. (Ed.). (2004b). The synaptic organization of the brain (5th ed.). Oxford: Oxford University Press.

    Google Scholar 

  • Shepherd, G. M. (2016). Foundations of the neuron doctrine (25th anniversary ed.). Oxford: Oxford University Press.

    Google Scholar 

  • Smith, C. U. (1992). A century of cortical architectonics. Journal of the History of the Neurosciences, 1, 201–218.

    Google Scholar 

  • Sober, E. (1999). The multiple realizability argument against reductionism. Philosophy of Science, 64, 542–564.

    Google Scholar 

  • Song, S., & Abbott, L. F. (2001). Cortical development and remapping through spike timing-dependent plasticity. Neuron, 32, 339–350.

    Google Scholar 

  • Sur, M. (1989). Visual plasticity in the auditory pathway: Visual inputs induced into auditory thalamus and cortex illustrate principles of adaptive organization in sensory systems. In M. A. Arbib & S. Amari (Eds.), Dynamic Interactions in Neural Networks: Models and Data (pp. 35–52). Berlin: Springer.

    Google Scholar 

  • Thomson Kelvin, W. (1842). On the uniform motion of heat in homogeneous solid bodies and its connection with the mathematical theory of electricity. Cambridge Mathematical Journal, 3, 71–84.

    Google Scholar 

  • Thomson Kelvin, W. (1855). On the theory of the electric telegraph. Proceedings of the Royal Society of London, 7, 382–399.

    Google Scholar 

  • Thomson, A. M., West, D. C., Wang, Y., & Bannister, P. (2002). Synaptic connections and small circuits involving excitatory and inhibitory neurons in layers 2–5 of adult rat and cat neocortex: Triple intracellular recordings and biocytin labelling in vitro. Cerebral Cortex, 12, 936–953.

    Google Scholar 

  • Turing, A. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 42, 230–265.

    Google Scholar 

  • Verplaetse, J., Schrijver, J. D., Vanneste, S., & Braeckman, J. (Eds.). (2009). The moral brain essays on the evolutionary and neuroscientific aspects of morality. Berlin: Springer.

    Google Scholar 

  • Vogt, C., & Vogt, O. (1903). Zur anatomische Gliederung des Cortex Cerebri. Journal für Psychologie und Neurologie, 2, 160–180.

    Google Scholar 

  • Vogt, C., & Vogt, O. (1919). Allgemeine Ergebnisse unserer Hirnforschung. Journal für Psychologie und Neurologie, 25, 279–461.

    Google Scholar 

  • von der Malsburg, C. (1973). Self-organization of orientation sensitive cells in the striate cortex. Kybernetic, 14, 85–100.

    Google Scholar 

  • von Economo, C., & Koskinas, G. N. (1925). Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. Berlin: Springer.

    Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442.

    Google Scholar 

  • Willshaw, D. J. (1972). A simple network capable of inductive generalization. Proceedings of the Royal Society of London B, 182, 233–247.

    Google Scholar 

  • Willshaw, D. (2006). Self-organization in the nervous system. In R. Morris & L. Tarassenko (Eds.), Cognitive systems: Information processing meets brain science (pp. 5–33). Amsterdam: Elsevier.

    Google Scholar 

  • Willshaw, D. J., Hallam, J., Gingell, S., & Lau, S. L. (1997). Marr’s theory of the neocortex as a self-organizing neural network. Neural Computation, 9, 911–936.

    Google Scholar 

  • Wilson, M. A., & Bower, J. M. (1989). The simulation of large-scale neural networks. In C. Koch & I. Segev (Eds.), Methods in neuronal modeling (pp. 291–333). Cambridge, MA: MIT Press.

    Google Scholar 

  • Wright, L. (1976). Teleological explanations. Berkeley, CA: University of California Press.

    Google Scholar 

  • Wright, J. J., & Bourke, P. D. (2017). Further work on the shaping of cortical development and function by synchrony and metabolic competition. Frontiers in Computational Neuroscience, 10, Article 127.

  • Young, M. P., Hilgetag, C. C., & Scannell, J. W. (2000). On imputing function to structure from the behavioural effects of brain lesions. Philosophical Transactions of the Royal Society B, 355, 147–161.

    Google Scholar 

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    Alessio Plebe

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Plebe, A. The search of “canonical” explanations for the cerebral cortex. HPLS 40, 40 (2018). https://doi.org/10.1007/s40656-018-0205-2

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